Current Research:

Complex Large-Scale Mobile Edge Computing Heterogeneous Networks


Description:

Mobile edge computing (MEC) is currently viewed as a prime emerging technology for the next generation mobile networks and Internet of Things (IoT). It allows to extend conventional cloud computing (CC) at the network edge to support real-time compute-intensive and high-bandwidth demanding mobile, IoT, and Big Data applications.

The architecture of MEC network contains an intermediate MEC layer between the cloud and end-users’ devices formed by distributed MEC servers with storage, computing, communication, and routing functions. MEC servers can be placed in stationary, quasi-stationary, and mobile locations, e.g., at the base stations (BSs), Wi-Fi access points (APs), unmanned aerial vehicles (UAVs), or users' mobile terminals.

Since MEC is still a very new concept, there are many open research problems that must be resolved to enable the deployment of MEC systems in real-world scenarios. Accordingly, this project aims to explore the design and implementation of large-scale MEC networks formed by numerous MEC servers that can be maintained by different mobile network operators (MNOs) and service providers (SPs) by addressing the following critical aspects:

1) resource management to guarantee the connectivity and required quality of service (QoS) for end-users given a decentralized nature of the network nodes controlled by different MNOs or SPs and the presence of incomplete (i.e., partially-observable) information about the network state;

2) content distribution and computational offloading to support efficient delivery of content and offloading of users’ computing tasks in a highly stochastic MEC environment with dynamic computing, caching, and spectrum resources and unstable wireless connections.

Grants:

NSFC Research Grant (2020-2022): Complex Large-Scale Mobile Edge Computing Heterogeneous Networks

Publications:

[1] A. Asheralieva and D. Niyato, “Bayesian Reinforcement Learning and Bayesian Deep Learning for Blockchains with Mobile Edge Computing,” IEEE Transactions on Cognitive Communications and Networking, Early Access, May 2020.

[2] A. Asheralieva and D. Niyato, “Combining Contract Theory and Lyapunov Optimization for Content Sharing With Edge Caching and Device-to-Device Communications,” IEEE/ACM Transactions on Networking, vol. 28, no. 3, pp. 1213-1226, June 2020.

[3] A. Asheralieva and D. Niyato, “Distributed Dynamic Resource Management and Pricing in the IoT Systems with Blockchain-as-a-Service and UAV-Enabled Mobile Edge Computing,” IEEE Internet of Things Journal, vol. 7, no. 3, pp. 1974-1993, March 2020.

[4] A. Asheralieva and D. Niyato, “Learning-Based Mobile Edge Computing Resource Management to Support Public Blockchain Networks,” IEEE Transactions on Mobile Computing, Early Access, Dec. 2019.

[5] A. Asheralieva and D. Niyato, “Hierarchical Game-Theoretic and Reinforcement Learning Framework for Computational Offloading in UAV-Enabled Mobile Edge Computing Networks with Multiple Service Providers,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8753-8769, Oct. 2019.

[6] A. Asheralieva and D. Niyato, “Game Theory and Lyapunov Optimization for Cloud-Based Content Delivery Networks with Device-to-Device and UAV-Enabled Caching,” IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 10094-10110, Oct. 2019.

[7] A. Asheralieva, “Optimal Computational Offloading and Content Caching in Wireless Heterogeneous Mobile Edge Computing Systems With Hopfield Neural Networks,” IEEE Transactions on Emerging Topics in Computational Intelligence, Early Access, Feb. 2019.

Collaborations:

Dusit (Tao) Niyato, Ph.D., IEEE Fellow, Professor
School of Computer Science and Engineering (SCSE) and School of Physical and Mathematical Sciences (SPMS)

Nanyang Technological University, Singapore
Webpage: https://www.ntu.edu.sg/home/dniyato/
 
 

Copyright © 2018 All Rights Reserved.